All-road-width flatness calculation method and device

Abstract

The invention discloses a full-road-width flatness calculation method and device. The method comprises the following steps: acquiring original elevation value point cloud data of a road surface; processing point cloud data noise; determining a lane category, calculating a wheel track distribution probability, and obtaining a longitudinal section weight; calculating a pavement equivalent longitudinal section elevation value of the lane based on the longitudinal section weight; and calculating a flatness index based on the equivalent vertical section elevation value. According to the method, comprehensive consideration of the use performance of the whole vehicle lane is realized, a brand-new full-road-width flatness calculation model is established, and more accurate and comprehensive road flatness evaluation can be provided.

Description

Technical Field

[0001] The invention relates to the field of highways, and in particular to a method and device for calculating the flatness of a full road width. Background Art

[0002] Road surface smoothness is a key indicator for measuring road performance. It objectively reflects the fluctuation of road elevation, thus revealing the driving quality and maintenance status of the road surface. The quality of smoothness is directly related to driving comfort and safety, and also profoundly affects the service life of the road surface, the degree of tire wear, and the fuel efficiency of the vehicle. Because of this, scientific and accurate testing of road surface smoothness plays an indispensable and important role in ensuring driving safety and improving road service levels.

[0003] There are many definitions of road surface smoothness. For example, the Permanent Technical Committee of the International Road Conference (PIARC) proposed a method for classifying road surface geometric characteristics based on geometric resolution at the 1987 World Road Congress in Brussels. The International Organization for Standardization (ISO) defines road surface smoothness as the deviation of the surface from the true plane surface in the ISO-13473 standard. The American Society for Testing and Materials (ASTM E 867) defines smoothness from the perspective of vehicle dynamics. In China, the traditional road smoothness evaluation method is generally based on the "Highway Roadbed and Pavement Field Test Procedure", which clearly stipulates three test methods and corresponding indicators for measuring smoothness, namely the three-meter ruler method, the continuous smoothness meter test method, and the vehicle-mounted bump accumulation meter test method. The "Highway Engineering Quality Inspection and Evaluation Standard" (JTG F80 / 1-2017) stipulates that the inspection of the smoothness index of each structural layer of each level of road is based on the maximum gap h (mm) between the three-meter ruler and the road surface, the standard deviation σ (mm) measured by the smoothness meter, and the international roughness index IRI (unit: m / km) as the standard. JTG 3450-2019 "Procedures for Field Testing of Highway Roadbed and Pavement" fully explains the experimental methods for measuring flatness using a three-meter ruler, a continuous flatness meter, a vehicle-mounted bump accumulation meter, and a vehicle-mounted laser flatness meter. Figure 1A-Figure 1C .

[0004] The detection methods of road surface roughness are mainly divided into subjective evaluation method, cross-section method and reaction method. Among them, the cross-section method, which involves the use of high-precision sensors such as laser profilers and three-dimensional laser radars to calculate the international roughness index IRI by measuring the elevation changes of the road surface section, is the most common method. The reaction method mainly relies on interdisciplinary research, because the various interactive phenomena of road / vehicle / user during vehicle driving jointly determine the performance of the road surface. In this process, complex methodologies are usually introduced to simulate the dynamic response of the vehicle as realistically as possible to evaluate its impact. However, whether it is laser cross-section detection technology or reaction detection method, they all rely on measuring the elevation changes on one or more measurement lines to determine the road surface roughness index. From a statistical point of view, the elevation changes of the road surface usually conform to the zero-mean normal distribution. However, due to factors such as vehicle rolling, the transverse and longitudinal slopes of the road, and weather conditions, the elevation distribution of the road in the transverse direction is not uniform. When different frequencies and different detection vehicles are traveling on the same road section, the detection trajectory of the laser probe is difficult to be completely consistent, which leads to low consistency of IRI detection results, thus affecting the analysis and evaluation of the flatness of the wide-area road network.

[0005] European and American countries have been researching 3D laser scanning technology since the 1960s, and have made significant progress in the 1990s. For example, airborne 3D laser scanning technology has been used to efficiently obtain large-area terrain information, realizing the industrialization of this technology. The research on road 3D laser scanning technology has also matured rapidly, such as related research at Stanford University and the University of Washington in the United States, and product development by well-known companies such as Leica and Rig. These devices have been widely used in production, life and scientific research, such as ancient building restoration, topographic mapping, road measurement, etc. Figure 2A-2B The present invention shows a vehicle-mounted three-dimensional road surface measuring device in the prior art.

[0006] Furthermore, in terms of three-dimensional measurement of road surfaces, Diaz et al. extracted a road surface measurement line 1m away from the outer edge of the road based on the calculation results of road linearity by using the road slice segmentation method and the K-means clustering method, and evaluated the flatness of the road surface by calculating the root mean square value, skewness, and kurtosis of the elevation of the measurement line. Kumar et al. proposed an algorithm that allows users to evaluate flatness from point clouds obtained through a three-dimensional measurement system. The purpose is to determine a method that can provide fast, cheap, and complete information to determine flatness by determining the standard deviation of the elevation value relative to the interpolation plane surface. Tavipu et al. developed a segmentation algorithm based on point cloud voxelization that can estimate the flatness of the road surface. Debilasis et al. used ordinary kriging and inverse distance weighted methods to process DSM data and extract road elevation changes to calculate IRI values, and compared them with the IRI data measured by the left and right wheels of the laser profiler. The standard deviation of the results was in the range of 1.07 to 1.83, proving that the results obtained by the two methods have a strong correlation. Barbara et al. calculated the road surface smoothness of the airport runway using LiDAR 3D point cloud data and DSM, and meticulously mapped the elevation and slope changes of different lateral measurement lines. Although this study considered the lateral impact of the smoothness distribution, it did not analyze whether there was a difference in the smoothness index obtained under different acquisition lines based on the smoothness parameters.

[0007] Existing studies have established mature and reliable point cloud data processing methods, proving the reliability of laser point cloud measurement technology as a means of detecting road surface roughness. However, due to the layout conditions of LiDAR and the drawing principle of point cloud data, it is difficult to directly read the elevation change of the vehicle wheel track position. In addition, existing studies often use the center line of the road wheel track, or the elevation of a fixed width measurement line on one side to calculate the roughness value, without fully considering the impact of the lateral roughness distribution of the road surface. Summary of the invention

[0008] In view of this, the present application proposes a method and device for calculating the smoothness of the entire road width, which can provide a more accurate and comprehensive road smoothness assessment.

[0009] According to one aspect of the present application, the present application provides a method for calculating the flatness of a full road width, the method comprising:

[0010] Obtain the original elevation value point cloud data of the road surface;

[0011] Dealing with point cloud data noise;

[0012] Determine the lane category, calculate the wheel track distribution probability, and obtain the longitudinal section weight;

[0013] Calculate the equivalent longitudinal section elevation value of the lane road surface based on the longitudinal section weight;

[0014] The flatness index is calculated based on the equivalent longitudinal section elevation value.

[0015] Preferably, processing point cloud data noise includes:

[0016] Remove point cloud data outside the lane boundary;

[0017] Eliminate abnormal data within the lane.

[0018] Preferably, determining the lane category includes determining the lane as a driving lane, a passing lane, a slow lane, or a mixed lane; the wheel track distribution probability is a lateral distribution probability of the wheel track;

[0019] Longitudinal section weight f norm (x) is calculated according to the following formula:

[0020]

[0021] Among them, f max and f min Respectively represent the maximum and minimum values ​​of f(x);

[0022]

[0023] Among them, ω i Represents μ i =0.5L i The weight of L i represents the actual wheelbase, x is the width of the lane in the lateral direction starting from the center of the lane, B i , μ i , and σ i is the fitting parameter.

[0024] Preferably, when the lane type is a driving lane, L i 1.8m; if the lane type is a passing lane, L i 1.6m; when the lane type is a slow lane, L i 2.0m; when the lane type is a mixed lane, L i is 1.8m.

[0025] Preferably, the equivalent longitudinal section elevation value P3(n) of the road surface of the lane is calculated according to the following formula:

[0026]

[0027] P2(x i ,n)=P1(x i ,n)·f norm (x i ),n=1,2,...,N;

[0028] P1(x i,n) is the point cloud data of each cross section, n represents the nth cross section along the lateral direction of the lane, and N represents the total number of cross sections covered by the point cloud.

[0029] Preferably, the method further comprises calculating the updated equivalent longitudinal section elevation value P4(n) of the lane according to the following formula:

[0030] P4(n)=a×P3(n) / M;

[0031] Among them, parameter M represents the number of sampling points in the cross section; parameter a is based on f norm The calculation is done by rounding off twice the ratio of the cumulative sum of (x) to M.

[0032] The present application provides a device for calculating the flatness of a whole road width, the device comprising:

[0033] An acquisition module is used to obtain the original elevation value point cloud data of the road surface;

[0034] The calculation module is used to process the noise of point cloud data; determine the lane category, calculate the wheel track distribution probability and the longitudinal section weight; calculate the equivalent longitudinal section elevation value of the lane road surface based on the longitudinal section weight; and calculate the flatness index based on the equivalent longitudinal section elevation value.

[0035] Preferably, the computing module is further used to remove point cloud data outside the lane boundary; and to remove abnormal data within the lane.

[0036] Preferably, the calculation module is further used to determine whether the lane is a driving lane, a passing lane, a slow lane or a mixed lane; calculate the lateral distribution probability of the wheel track;

[0037] The longitudinal section weight f is calculated according to the following formula norm (x):

[0038]

[0039] Among them, f max and f min Respectively represent the maximum and minimum values ​​of f(x);

[0040]

[0041] Among them, ω i Represents μ i =0.5L i The weight of L i represents the actual wheelbase, x is the width of the lane in the lateral direction starting from the center of the lane, B i , μ i , and σ i is the fitting parameter.

[0042] Preferably, when the lane type is a driving lane, Li 1.8m; if the lane type is a passing lane, L i 1.6m; when the lane type is a slow lane, L i 2.0m; when the lane type is a mixed lane, L i is 1.8m.

[0043] Preferably, the calculation module is further used to calculate the equivalent longitudinal section elevation value P3(n) of the road surface of the lane according to the following formula:

[0044]

[0045] P2(x i ,n)=P1(x i ,n)·f norm (x i ),n=1,2,...,N;

[0046] P1(x i ,n) is the point cloud data of each cross section, n represents the nth cross section along the lateral direction of the lane, and N represents the total number of cross sections covered by the point cloud.

[0047] Preferably, the calculation module is further used to calculate the updated equivalent longitudinal section elevation value P4(n) of the lane according to the following formula:

[0048] P4(n)=a×P3(n) / M;

[0049] Among them, parameter M represents the number of sampling points in the cross section; parameter a is based on f norm The calculation is done by rounding off twice the ratio of the cumulative sum of (x) to M.

[0050] Compared with the traditional technical solution that only focuses on the wheel track, this application realizes a comprehensive consideration of the performance of the entire lane, establishes a new full-width flatness calculation model, and can provide a more accurate and comprehensive road flatness evaluation. The statistical principle is introduced, and a weight model of the road longitudinal section is innovatively established. The model can scientifically and reasonably allocate the weights of each longitudinal section within the entire road width, and generate an equivalent longitudinal section that can represent the flatness of the entire road width through the weight model, thereby improving the accuracy and comprehensiveness of the flatness evaluation.

[0051] Other features and advantages of the present application will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The accompanying drawings constituting a part of the present application are used to provide a further understanding of the present application, and the schematic implementation modes and descriptions of the present application are used to explain the present application. In the accompanying drawings:

[0053] Figure 1A-Figure 1CIt is a road surface flatness measuring instrument in the prior art; wherein Figure 1A A three-meter ruler, Figure 1B It is a continuous eight-wheel road roughness tester. Figure 1C It is a vehicle-mounted laser flatness meter;

[0054] Figure 2A-2B It is a vehicle-mounted road 3D measurement device in the prior art, wherein Figure 2A The schematic diagram of the equipment structure and measurement principle is shown in Figure 2. Figure 2B It is a pseudo-color image of the road surface three-dimensional point cloud data;

[0055] Figure 3 Flow chart of the method for calculating the flatness of the entire road width provided for this application;

[0056] Figure 4 The fitting results of the wheel track centerline in BS 5400-10 provided for this application;

[0057] Figure 5 The wheel track lateral distribution probability curve provided for this application;

[0058] Figure 6 is the vehicle type of each lane under the influence of channelization in the prior art;

[0059] Figure 7 The weight f provided for this application norm The ratio of the cumulative sum of (x) to the number of sampling points N;

[0060] Figure 8 Comparison of the mean values ​​of IRI calculated based on two elevation values ​​provided in this application;

[0061] Fig. 9 A comparison of coefficients of variation based on repeatability measurements of two elevation values ​​is provided for this application. DETAILED DESCRIPTION

[0062] It should be noted that, in the absence of conflict, the embodiments of the present application and the features in each embodiment may be combined with each other.

[0063] The present application will be described in detail below with reference to the accompanying drawings and in combination with implementation modes.

[0064] At present, smoothness measurement equipment is mainly divided into two categories: two-dimensional and three-dimensional. Two-dimensional detection equipment measures the elevation value by selecting a longitudinal section of the wheel track, but this method is random, resulting in poor repeatability in measuring elevation values ​​and calculating IRI, which may lead to incorrect evaluation of road performance. Three-dimensional detection equipment was not originally developed specifically for road surface smoothness, but was used to detect local damage such as potholes and bumps on the road surface. Although some researchers have proposed using three-dimensional point cloud data to analyze smoothness, they still focus on the elevation value of a longitudinal section in the wheel track.

[0065] For channelized roads, vehicle driving trajectories are regular, and wheel track distribution is "M"-shaped, which proves the rationality of using the elevation value of a certain longitudinal section within the wheel track to calculate the smoothness in the early stage. However, the smoothness of a single wheel track cannot fully measure the performance of the lane, nor can it represent the impact of the lane on the smoothness of the vehicle.

[0066] In the early days, it was challenging to use two-dimensional measurement equipment to comprehensively measure the elevation values ​​of lanes across the entire road width. However, with the promotion of three-dimensional measurement equipment, it has become possible to comprehensively consider the flatness of the entire lane. After obtaining the point cloud data of the elevation values ​​of the entire road width, how to reasonably allocate the weights of each longitudinal section of the lane to reasonably evaluate the lane flatness is an urgent problem to be solved.

[0067] This application aims to propose the concepts of road longitudinal section weight model and lane equivalent longitudinal section based on road surface 3D point cloud data, and establish a calculation model for equivalent longitudinal section elevation value. Through this model, we will replace the earlier practice of relying only on wheel track longitudinal section and its elevation value, and then build a full-width flatness calculation model for the entire lane. This model will be able to more comprehensively and scientifically consider the performance of the entire lane, and provide more accurate and comprehensive performance parameter basis for traffic management departments.

[0068] See the flowchart of the full road width flatness calculation method proposed in this application. Figure 3 The main steps involved are as follows:

[0069] Step 301, obtaining original elevation value point cloud data of the road surface;

[0070] Step 302, point cloud data noise processing: including road boundary recognition and lane outlier removal;

[0071] Step 303, determine the lane category, calculate the wheel track distribution probability and longitudinal section weight;

[0072] Step 304, calculating the equivalent longitudinal section elevation of the road surface of the lane;

[0073] Step 305, calculating the flatness index such as IRI based on the equivalent longitudinal section elevation value.

[0074] First, the original elevation value point cloud data of the road surface is obtained through 3D laser scanning or other data acquisition equipment. Then, the boundaries of the road are identified and determined to limit the scope of the subsequent smoothness analysis. Next, in order to limit the scope of the subsequent smoothness analysis, the boundaries of the road need to be identified and determined. Subsequently, these raw data are preprocessed, which includes identifying and removing outliers in the lane, which may be caused by equipment errors or foreign objects on the road surface. After cleaning, the valid point cloud data in the lane is retained as a solid foundation for the subsequent smoothness analysis.

[0075] Furthermore, in order to more accurately analyze the road surface flatness, weights are assigned to the point cloud data of each longitudinal section, and the equivalent elevation value of each longitudinal section is calculated using the weight model. Subsequently, the equivalent elevation values ​​of each cross section are accumulated. On this basis, the maximum and minimum values ​​are found from the original valid point cloud elevation values, and then the equivalent elevation values ​​are normalized with these two values ​​as the target to reduce the aforementioned distortion of the elevation value caused by accumulation.

[0076] Finally, based on the normalized elevation values, the International Roughness Index (IRI) is derived using a general calculation method, and this index is used as the core indicator for evaluating road surface roughness and testing equipment.

[0077] The main steps involved in the above calculation process are analyzed and explained below.

[0078] In step 301, the present application uses the existing technology to obtain the original elevation point cloud data of the road surface, and uses a three-dimensional point cloud acquisition device to collect road surface elevation information for the test road. When measuring the lane elevation point cloud data, the boundary of the point cloud can cover the entire lane, which is convenient for the subsequent extraction of the lane edge line from the point cloud data.

[0079] In step 302, the collected raw data is inevitably mixed with a lot of useless interference noise, which mainly comes from the environment outside the road lane and occasional abnormal points or singular points inside the lane, or caused by the equipment itself. These noise data may affect the accuracy of subsequent analysis, so effective data cleaning must be performed.

[0080] First, identifying lane boundaries is a key step in point cloud data noise processing. By analyzing the spatial distribution of point cloud data and road surface characteristics, the range of lanes and non-lane areas can be clearly distinguished. This step provides the necessary spatial definition for data cleaning and subsequent data processing. Then, all point cloud data outside the lane boundaries are removed.

[0081] Furthermore, the abnormal data in the lane is removed. Considering the continuity of point cloud data, it can be used to carefully remove the occasional singular points in the lane. Such a processing step is crucial to improving the overall quality of the data because it can eliminate outliers that may interfere with data analysis.

[0082] Ultimately, after screening and cleaning, the retained point cloud data will be limited to valid information within the lane. This data will provide an accurate and reliable basis for road elevation analysis, flatness assessment, and road condition monitoring.

[0083] This application does not limit the specific form and method of processing point cloud data noise. The two key points are: 1: High-precision identification of the left and right boundaries (sidelines) of the lane, and elimination of point cloud data outside the lane; 2: Based on the continuity of elevation values ​​and other characteristics of the point cloud data within the lane, outliers are eliminated or repaired.

[0084] In step 303, during the actual vehicle driving process, the distribution of wheel tracks on the lane cross section shows a certain regularity. The wheel track lateral distribution coefficient is an indicator that describes the distribution regularity of vehicle wheel tracks on the road cross section. It refers to the ratio of the number of actions on the unit width of the road surface to the total number of wheel load actions within the lane width. Factors affecting this coefficient include traffic organization and management, traffic volume, traffic composition, lane width, vehicle speed, and driver's driving habits. These factors lead to significant differences in wheel track lateral distribution coefficients under different highway grades and lane types.

[0085] The wheel track lateral distribution probability curve is a key tool to characterize the contact frequency between the left and right wheels of a vehicle and each longitudinal section of the ground. This curve reveals the probability of different road surface positions being crushed by the wheels during the driving process of the vehicle. Specifically, the high or low wheel track distribution probability value is directly related to the degree of compaction of the wheel on a specific longitudinal section of the road surface, which in turn shows the excitation effect of a specific longitudinal section of the road on the vehicle. A high probability value indicates that the wheel frequently passes through the longitudinal section, increasing the possibility of forming a wheel track belt; while a low probability value means that the wheel has less compaction effect on the longitudinal section. In areas with smaller wheel track distribution probability values, not only the chance of the wheel passing through is reduced, but also the reaction force generated by the wheel on the road surface will be reduced accordingly. Based on the correlation between the wheel track lateral distribution probability curve and the road load and the reaction to the vehicle, this application selects this probability model as the theoretical basis model for lane longitudinal section weight distribution. This choice is based on a deep understanding of vehicle driving behavior and road response characteristics. The wheel track lateral distribution probability model is used as the longitudinal section weight allocation model, which not only reasonably considers the actual effect of vehicles on the road surface, but also scientifically quantifies the importance of different longitudinal sections, providing accurate weights for the evaluation of road surface smoothness.

[0086] At present, the public standards and specifications involving vehicle driving track distribution include the European standard Eurocode1 and the British standard BS 5400-10. Among them, the British standard BS 5400-10 gives a normal distribution of the vehicle centerline lateral distribution frequency model based on observed statistical data (the lateral wheel track loading frequency is given according to the strip width of 0.1m). The lateral distribution width of the wheel track centerline in the lane surface of both is 1.2m, such as Figure 4 Considering the wide application and applicability of this specification, this application sets it as a standard reference. The statistical probability data corresponding to the 13 strips in the figure are fitted according to the normal distribution, and the results are shown in Figure 4 The normal distribution model of wheel track centerline distribution probability is shown in formula (1).

[0087]

[0088] Among them, the fitting parameters are B=0.098674, μ=0, σ=0.226058.

[0089] According to R 2 =0.9957, it can be seen that the fitting effect is good, indicating that formula (1) has a high model universality, and it is scientific to use it to derive the lateral distribution probability of the left and right wheel tracks in the subsequent process.

[0090] Considering that in each independent lane, the left and right wheel tracks are directly related to the wheelbase of the vehicle. However, the wheelbase of the vehicle is not a uniform standard, and its common range covers 1.5m to 2.2m. Without loss of generality, this application uses 1.6m, 1.8m and 2.0m as three typical wheelbase intervals to analyze the probability distribution of the left and right wheel tracks along the lane width direction under the above wheel track centerline distribution frequency. The results are shown in Figure 5 .

[0091] exist Figure 5 In the figure, the lateral distribution probability of the left and right wheel tracks under the three wheel widths all show a typical "M" shape. The two-dimensional Gaussian mixture model shown in formula (2) is used to fit it. The parameters to be fitted are B1, B2, μ1, μ2, σ1 and σ2. Among them, B1 and B2 are two Gaussian models f(x)~N1(μ1, σ1 2 ) and f(x)~N2(μ2,σ2 2 ). Considering Figure 5 The symmetry of the curve in , we can assume that B1 = B2, μ1 = μ2, σ1 = σ2. At this time, formula (2) is further simplified to formula (3). Finally, the fitting results are shown in Table 1.

[0092]

[0093] Table 1 Lateral distribution frequency of left and right wheel tracks in the lane at different wheel widths

[0094] L(m) <![CDATA[B1]]> <![CDATA[μ1]]> <![CDATA[σ1]]> <![CDATA[R 2 ]]> 1.6 0.098669 0.800 0.226 0.9999 1.8 0.098669 0.900 0.226 0.9999 2.0 0.098671 1.000 0.226 0.9999

[0095] As can be seen from Table 1, the fitting parameters B1 and σ1 remain basically unchanged under the three different wheelbase conditions, while the parameter μ1 changes with the change of wheelbase L, and there is a significant 2-fold relationship between the two. Such observations show that for vehicles with the same wheelbase, the lateral probability distribution of the left and right wheel tracks is consistent. However, it must be pointed out that the above three wheelbases cannot represent the wheelbase conditions of all vehicles traveling on a specific lane. On actual roads, the formation of the wheel track is caused by the rolling of all passing vehicles in the lane. This means that the formation process of the wheel track involves vehicles with more wheelbases. However, due to the non-standardized characteristics of the wheelbase, the specific position and width of the wheel track are also variable. In this case, the model of the lateral distribution frequency of the wheel track can be summarized as formula (4).

[0096]

[0097] In the formula, ω i Represents μ i =0.5L i The weight of L i Indicates the actual wheelbase.

[0098] In addition, the lane channelization effect has a significant impact on the vehicles traveling in each lane, making the types of vehicles in each lane relatively universal. Taking the common three-lane single-sided road as an example, the leftmost lane is usually the overtaking lane, which is mainly used for high-speed overtaking; the middle lane is the driving lane, which is the main channel for most vehicles; and the rightmost lane is the slow lane, which is often used for heavy-loaded trucks to travel at low speeds. Figure 6 This lane channelization effect not only affects the driving behavior of vehicles, but also has an important impact on the formation and characteristics of wheel tracks.

[0099] Specifically, in the overtaking lane, since it is mainly composed of small cars with a small wheelbase, the lateral distribution probability curve of the left and right wheel tracks will tend to the situation when the wheelbase L = 1.6m. In the driving lane, the types of vehicles are relatively more diverse, but they are mainly trucks with a medium wheelbase, which makes the lateral probability distribution curve of the left and right wheel tracks closer to the situation when the wheelbase L = 1.8m. In the slow lane, since it is mainly composed of trucks with a wide wheelbase, the lateral distribution probability curve of the wheel track will tend to the situation when the wheelbase L = 2.0m. This difference in vehicle type distribution caused by the lane channelization effect further affects the formation and characteristics of the wheel track.

[0100] In mixed traffic environments, especially in urban traffic flows, vehicle types are more diverse than on highways, which leads to a higher complexity in the lateral distribution of vehicle tracks. The lateral distribution probability of vehicle tracks is not only related to the vehicle type, but also affected by factors such as the number of vehicles passing, traffic density, and vehicle behavior patterns. Since the wheelbases of different types of vehicles are different, it becomes challenging to establish a universal weight model to accurately quantify the impact of vehicles with different wheelbases on the road surface smoothness. Such a model needs to consider the traffic characteristics under specific road conditions, which vary from road to road and are difficult to quantify uniformly. Despite the above complexity, through field observation and analysis of actual roads, visual features such as color and texture can be used to effectively distinguish between wheel track strips and non-wheel track strips. Such physical facts show that in mixed traffic environments, the wheel track distribution probability model of each lane also conforms to formula (3).

[0101] In view of this, when the point cloud measurement technology is used to collect the road elevation values ​​of different lanes, the parameter μ i (or L i ) for solidification. The specific solidification values ​​can be found in Table 2. By solidifying the parameters, it can be ensured that different devices and manufacturers can have a unified calculation benchmark when processing these point cloud data. In this way, it can not only effectively improve the comparability of the data, but also provide a more solid and reliable foundation for subsequent data analysis and decision-making.

[0102] Table 2 Recommended μ for different lane types i Value and L value

[0103] Overtaking Lane Driving lane Slow Lane Mixed lanes <![CDATA[L i (m)]]> 1.6 1.8 2.0 1.8 <![CDATA[μ i (m)]]> 0.8 0.9 1.0 0.9

[0104] In addition, in order to introduce the weight model f(x) into the subsequent analysis and calculation of the cross-section elevation value, f(x) needs to be transformed again. The core goal of this transformation is to strengthen the elevation value representation on the wheel track while weakening the influence of the elevation value in the middle and on both sides of the lane. To achieve this goal, f(x) should be adjusted to a function with 1 as the maximum value and 0 as the minimum value.

[0105] Specifically, the transformation of f(x) should follow the following principles: During vehicle driving, the wheel track is the key area that affects the dynamic response of the vehicle, so the elevation value of this area needs to be clearly represented and retained; while the area in the middle and on both sides of the lane has little effect on vehicle driving, and the corresponding elevation values ​​can be suppressed by mathematical methods. The overall idea of ​​this transformation is to normalize f(x) so that its value range is mapped to the interval [0,1], where 1 represents the maximum weight of the wheel track and 0 represents the minimum weight in the middle or on both sides of the lane. The normalization process is shown in formula (5).

[0106]

[0107] In the formula, f max and f min Represent the maximum and minimum values ​​of f(x) respectively.

[0108] Through the above normalization process, f(x) will be effectively converted into a weight function suitable for cross-section elevation value analysis.

[0109] In step 304, after successfully acquiring the valid point cloud data in the lane and establishing the longitudinal section weight model of the lane, the equivalent elevation value of the lane data can be calculated by the following steps:

[0110] (1) According to the specific type of the lane being detected, the key parameters L and μ of the longitudinal section weight model are determined, and the corresponding data sequence f is generated according to the lateral point cloud density. norm (x i ), x i The range is [0,L].

[0111] (2) Along the driving direction, the point cloud data P1(x i ,n) and the longitudinal section weight model sequence f norm (x i ) performs point-by-point multiplication to generate the weighted elevation values ​​P2(x i ,n), see formula (6). In the formula, n represents the nth cross section along the driving direction, and N represents the total number of cross sections covered by the point cloud.

[0112] (3) The cross-section data after the product operation are accumulated and summed to obtain the comprehensive elevation value P3(n) of each cross-section. The equivalent longitudinal section elevation value P3(n) is the sum of the elevation values ​​P1(x i ,n) and its weight f norm (x i ).

[0113] P2(x i ,n)=P1(x i ,n)·f norm (x i ),n=1,2,...,N (6)

[0114]

[0115] Theoretically, once the equivalent longitudinal elevation value P3(n) is obtained, the IRI and other indicators can be derived according to the general standard flatness calculation method. However, the calculation result may not fully reflect the actual use of the road surface, so it may lack sufficient reference value and guiding significance in practical applications. According to the product process described by formula (6), the longitudinal section weight model fnorm (x) has an impact on the elevation point cloud data. Although this process introduces the distribution of weights, it may change the amplitude of the elevation value. In addition, according to the superposition process described by formula (7), a large number of elevation values ​​are comprehensively considered to calculate the equivalent elevation value. Although this process can provide an aggregated representation of the data, it may also over-emphasize the aggregation effect of the data, thereby weakening some important detail information.

[0116] Further analysis found that when different sampling rates are used to sample elevation data for the same cross section, the difference in the number of sampling points will cause significant changes in the elevation value P3(n). This fluctuation in the equivalent longitudinal elevation value caused by changes in sampling density may not accurately reflect the actual flatness of the road surface, and is contrary to the original intention of this application to accurately evaluate the flatness of the road surface and provide guidance for road maintenance. Based on this, this application proposes a strategy for scaling the equivalent elevation value P3(n) to map it to a scientific and reasonable numerical range.

[0117] Figure 7 It shows that the weight model f is norm The ratio of the sum of (x) to the number of sampling points M in the cross section. The number of sampling points M is indirectly represented by the sampling interval Δx. As can be seen from the figure, no matter how the sampling interval changes, f norm The ratio of the cumulative sum of (x) to M tends to a stable value, namely 0.3226. In addition, the standard deviation of the ratio is 0.00455, and the coefficient of variation is only 1.41%, indicating that the results under different sampling intervals have high consistency. Based on this significant and stable phenomenon, the present application further proposes a scaling processing method for equivalent longitudinal section elevation values ​​based on observed data. Specifically, the equivalent longitudinal section elevation value is scaled using formula (8) to ensure that the weight model can maintain the validity and reliability of its analysis under different sampling intervals. This scaling process not only helps to eliminate the impact of sampling interval changes on model results, but also improves the generalization ability of the model under different conditions. Finally, the equivalent longitudinal section elevation value of a lane is represented by P4(n).

[0118] P4(n)=a×P3(n) / M (8)

[0119] In the formula, parameter M represents the number of sampling points in the cross section; parameter a = 6, which is obtained by rounding the reciprocal of the ratio 0.3226 to an integer. This calculation method ensures that the scaling process can match the theoretically observed stable ratio, and through the rounding process, the operability of the parameter in practical applications is enhanced.

[0120] In step 305, after obtaining the equivalent longitudinal elevation value of the road surface of the lane, the flatness index can be derived according to the public flatness calculation method, such as the International Roughness Index IRI, etc. It should be noted that the present invention does not make any innovation in the method of calculating flatness based on elevation values, but directly adopts the current common flatness calculation standards and methods.

[0121] In view of the shortcomings of the prior art that only a single wheel track strip longitudinal section is used for measurement and evaluation, this application proposes a new concept of full-width road flatness. Corresponding calculation models and methods have been developed, which can comprehensively and comprehensively evaluate the overall flatness of the measured road and overcome the limitations of the prior art. Statistical principles are introduced, and a weighted theoretical model of road longitudinal sections is established based on existing specification documents. This model can reasonably allocate the weights of each longitudinal section within the entire road width to ensure the accuracy and comprehensiveness of the flatness evaluation. By extracting the lane edge lines in the preprocessing stage, the point cloud range of the entire lane is limited, and the position accuracy of each longitudinal section of the road is enhanced. Based on the aforementioned weight model, an equivalent longitudinal section that can represent the flatness of the entire road width is generated. The elevation value of the equivalent longitudinal section of the measured lane is calculated as a key indicator for evaluating road flatness, replacing the practice of only considering a random longitudinal section within the wheel track strip in the prior art.

[0122] A vehicle-mounted 3D point cloud measurement device and a vehicle-mounted laser roughness meter were used to measure the standard test road of the Ministry of Transport's highway traffic test site in Tongzhou District, Beijing. When collecting the road elevation, both vehicles drove along the center line of the lane. The laser roughness meter selected the data at the left wheel track strip longitudinal section V1 for analysis and marked it as IRI0; after processing the data, the vehicle-mounted 3D point cloud measurement device selected the left wheel track strip longitudinal section V2 and the lane equivalent longitudinal section for calculation and analysis, which were marked as IRI V and IRI F The method of multiple repeated measurements was adopted to ensure the accuracy of the elevation data and the reliability of the proposed flatness method. The test road was measured repeatedly 9 times, and the average of the elevation values ​​was used to calculate the corresponding IRI0, IRI V and IRI F .

[0123] Figure 8 The display shows the elevation values ​​of the V1 longitudinal section, V2 longitudinal section and equivalent longitudinal section, and the flatness index IRI0 and IRI calculated for every 100m length. V and IRI FFirst, compare the measurement results of the two types of equipment on the left wheel track longitudinal sections V1 and V2. The two curves are obviously consistent, but there are local differences, indicating that the elevation values ​​measured by the two measurement methods are indeed different. Due to the lack of standard values, it is impossible to say which measurement result is the best. However, from the source of data acquisition, it can be seen that the IRI calculated based on the V2 longitudinal section is V More in line with expectations.

[0124] In addition, from the data trend analysis, the flatness index obtained using two different elevation values ​​in the point cloud data showed good consistency, with a correlation coefficient of 0.996. However, it is worth noting that IRI F The values ​​are generally higher than IRI V . The reason for this phenomenon is that the equivalent elevation value of the longitudinal section is more complex and comprehensive. It not only covers the elevation data of the V2 longitudinal section, but also integrates the elevation information of multiple other longitudinal sections in the lane. Therefore, compared with relying only on the elevation data of a single longitudinal section, the equivalent elevation value undoubtedly provides richer and more comprehensive road elevation information. This information gain is reflected in the calculation of the flatness index, making the flatness index value obtained based on the equivalent elevation value relatively large. This phenomenon not only proves the superiority of the equivalent elevation value in reflecting the road condition, but also provides a more accurate and comprehensive method for road flatness evaluation.

[0125] Subsequently, the coefficient of variation Cv was used to analyze the repeatability of the calculated results, and the results are shown in Fig. 9 In the two longitudinal sections based on the 3D point cloud device, the coefficient of variation Cv is relatively small, less than 3.0% and 4.0%, respectively. However, the output results of the V1 longitudinal section measured by the vehicle-mounted laser roughness meter show a large variability. This result shows that the use of point cloud data for road surface roughness measurement and analysis is a feasible method. In particular, even if only the elevation value of the V2 longitudinal section is used, a stable IRI calculation result can be obtained. This stability is due to the application of 3D point cloud technology. During measurement, the position of each longitudinal section in the lane is limited by the road edge line, thereby improving the accuracy and consistency of the measurement. Compared with the traditional vehicle-mounted laser roughness meter, which collects elevation data relatively randomly within the wheel track, the position of the V2 longitudinal section in the lane is predetermined, which increases the predictability and accuracy of the measurement.

[0126] In addition, when the equivalent longitudinal elevation is used to calculate the IRI, the variability of the data measurement results reflects the stability and reliability of the performance of the measuring instrument when repeatedly measuring the same road section. Since the equivalent longitudinal elevation integrates the data of all relevant longitudinal sections and provides a comprehensive description of the road surface morphology, the stability of the measurement results is affected by the comprehensive data.

[0127] The above description is only a preferred implementation mode of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for calculating the flatness of the entire road width, characterized in that: The method includes: Obtain the original elevation value point cloud data of the road surface; Dealing with point cloud data noise; Determine the lane category, calculate the wheel track distribution probability, and obtain the longitudinal section weight; Calculate the equivalent longitudinal section elevation value of the lane road surface based on the longitudinal section weight; The flatness index is calculated based on the equivalent longitudinal section elevation value.

2. The method for calculating the flatness of the entire road width according to claim 1, characterized in that: Processing point cloud data noise includes: Remove point cloud data outside the lane boundary; Eliminate abnormal data within the lane.

3. The method for calculating the flatness of the entire road width according to claim 1, characterized in that: Determining the lane category includes determining whether the lane is a driving lane, an overtaking lane, a slow lane, or a mixed lane; the wheel track distribution probability is the lateral distribution probability of the wheel track; Longitudinal section weight f norm (x) is calculated according to the following formula: Among them, f max and f min Respectively represent the maximum and minimum values ​​of f(x); Among them, ω i Represents μ i =0.5L i The weight of L i represents the actual wheelbase, x is the width of the lane in the lateral direction starting from the center of the lane, B i , μ i , and σ i is the fitting parameter.

4. The method for calculating the flatness of the entire road width according to claim 3, characterized in that: When the lane type is a driving lane, L i 1.8m; If the lane type is a passing lane, L i 1.6m; When the lane type is a slow lane, L i 2.0m; When the lane type is a mixed lane, L i is 1.8m.

5. The method for calculating the flatness of the entire road width according to claim 3, characterized in that: The equivalent longitudinal section elevation value P3(n) of the lane road surface is calculated according to the following formula: P2(x i ,n)=P1(x i ,n)·f norm (x i ),n=1,2,...,N; P1(x i ,n) is the point cloud data of each cross section, n represents the nth cross section along the lateral direction of the lane, and N represents the total number of cross sections covered by the point cloud.

6. The method for calculating the flatness of the entire road width according to claim 5, characterized in that: The method also includes calculating the updated equivalent longitudinal elevation value P4(n) of the road surface of the lane according to the following formula: P4(n)=a×P3(n) / M; Among them, parameter M represents the number of sampling points in the cross section; parameter a is based on f norm The calculation is done by rounding off twice the ratio of the cumulative sum of (x) to M.

7. A device for calculating the flatness of a whole road width, characterized in that: The device includes: An acquisition module is used to obtain the original elevation value point cloud data of the road surface; The calculation module is used to process the point cloud data noise; determine the lane category, calculate the wheel track distribution probability and the longitudinal section weight; calculate the equivalent longitudinal section elevation value of the lane road surface based on the longitudinal section weight; and calculate the flatness index based on the equivalent longitudinal section elevation value.

8. The device for calculating the flatness of the entire road width according to claim 7, characterized in that: The calculation module is also used to remove the point cloud data outside the lane boundary and eliminate abnormal data within the lane.

9. The device for calculating the flatness of the entire road width according to claim 7, characterized in that: The calculation module is also used to determine whether the lane is a driving lane, an overtaking lane, a slow lane or a mixed lane; calculate the lateral distribution probability of the wheel track; The longitudinal section weight f is calculated according to the following formula norm (x): Among them, f max and f min Respectively represent the maximum and minimum values ​​of f(x); Among them, ω i Represents μ i =0.5L i The weight of L i represents the actual wheelbase, x is the width of the lane in the lateral direction starting from the center of the lane, B i , μ i , and σ i is the fitting parameter.

10. The device for calculating the flatness of the entire road width according to claim 9, characterized in that: When the lane type is a driving lane, L i 1.8m; If the lane type is a passing lane, L i 1.6m; When the lane type is a slow lane, L i 2.0m; When the lane type is a mixed lane, L i is 1.8m.

11. The device for calculating the flatness of the entire road width according to claim 9, characterized in that: The calculation module is also used to calculate the equivalent longitudinal section elevation value P3(n) of the lane according to the following formula: P2(x i ,n)=P1(x i ,n)·f norm (x i ),n=1,2,...,N; P1(x i ,n) is the point cloud data of each cross section, n represents the nth cross section along the lane, and N represents the total number of cross sections covered by the point cloud.

12. The device for calculating the flatness of the entire road width according to claim 11, characterized in that: The calculation module is also used to calculate the updated equivalent longitudinal section elevation value P4(n) of the lane according to the following formula: P4(n)=a×P3(n) / M; Among them, parameter M represents the number of sampling points in the cross section; parameter a is based on f norm The calculation is done by rounding off twice the ratio of the cumulative sum of (x) to M.

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